An Evaluation of Two NEXRAD Wind Retrieval Methodologies and Their Use in Atmospheric Dispersion Models

Two entirely different methods for retrieving 3D fields of horizontal winds from Next Generation Weather Radar (NEXRAD) radial velocities have been evaluated using radar wind profiler measurements to determine whether routine wind retrievals would be useful for atmospheric dispersion model applications. The first method uses a physical algorithm based on four-dimensional variational data assimilation, and the second simpler method uses a statistical technique based on an analytic formulation of the background error covariance. Both methods can be run in near–real time, but the simpler method was executed about 2.5 times as fast as the four-dimensional variational method. The observed multiday and diurnal variations in wind speed and direction were reproduced by both methods below 1.5 km above the ground in the vicinity of Oklahoma City, Oklahoma, during July 2003. However, wind retrievals overestimated the strength of the nighttime low-level jet by as much as 65%. The wind speeds and directions obtained from both methods were usually similar when compared with profiler measurements, and neither method outperformed the other statistically. Within a dispersion model framework, the 3D wind fields and transport patterns were often better represented when the wind retrievals were included along with operational data. Despite uncertainties in the wind speed and direction obtained from the wind retrievals that are higher than those from remote sensing radar wind profilers, the inclusion of the wind retrievals is likely to produce more realistic temporal variations in the winds aloft than would be obtained by interpolation using the available radiosondes, especially during rapidly changing synoptic- and mesoscale conditions.- Reference

Maximum Position Alignment Method for Noisy High-Resolution Radar Target Classification

In this paper, the alignment of noisy high-resolution radar signals using the maximum position method is studied. The relationship between the shift estimation and the signal-to-noise ratio is considered. As a result, two analytical expressions are obtained that approximate the root-mean-square error of the difference in the shift estimation with and without noise. These two expressions allow us to improve the understanding of the sensitivity to noise of the Maximum Position alignment method. - Reference

Predicted Detection Performance of MIMO Radar

It has been shown that multiple-input multiple-output (MIMO) radar systems can improve target detection performance significantly by exploiting the spatial diversity gain. We introduce the system model in which the radar target is composed of a finite number of small scatterers and derive the formula to evaluate the theoretical probability of detection for the system having an arbitrary array-target configuration. The results can be used to predict the detection performance of the actual MIMO radar without time-consuming simulations. - Reference

Radar Revisited (review of "Radar Handbook, 3rd ed." by Merrill Skolnik) [Book Reviews]

This is the third edition of an established handbook, edited by one of the most-recognized names in the field of radar technology. The volume is a compilation of 26 chapters, authored by individuals with a thorough command of, and incredible credentials in, the topics of their chapters. Most chapters have a large number of figures (up to several dozen) and extensive bibliographies. Chapters range from fairly quantitative and mathematical ones to cursory and descriptive ones. Some sections of the handbook represent a concise and readable summary of the state-of-the-art of knowledge on their topics; others are a sketchy collection of remarks for which it is difficult to identify the benefits to be derived by the reader. There is little coordination between chapters where similar topics may be discussed, and a lack of any cross-referencing. There are also weaknesses in the index, as well. While the older, classical radar topics receive much attention, the book overlooks newer areas such as coverage of automotive radars. This volume will appeal to the generalists with interest in the conventional radar subjects, and to others as a starting point for locating sources with more detailed information. - Reference

An Orientation-Selective Orthogonal Lapped Transform

A novel critically sampled orientation-selective orthogonal lapped transform called the lapped Hartley transform (LHT) is derived. In a first step, overlapping basis functions are generated by modulating basis functions of a 2-D block Hartley transform by a cosine wave. To achieve invertibility and orthogonality, an iterative filter is applied as prefilter in the analysis and as postfilter in the synthesis operation, respectively. Alternatively, filtering can be restricted to analysis or synthesis, ending up with a biorthogonal transform (LHT-PR, LHT-PO). A statistical analysis based on a 4000-image data base shows that the LHT and LHT-PO have better redundancy removal properties than other block or lapped transforms. Finally, image compression and noise removal examples are given, showing the advantages of the LHT especially in images containing oriented textures. - Reference

Texture Analysis and Classification With Linear Regression Model Based on Wavelet Transform

The wavelet transform as an important multiresolution analysis tool has already been commonly applied to texture analysis and classification. Nevertheless, it ignores the structural information while capturing the spectral information of the texture image at different scales. In this paper, we propose a texture analysis and classification approach with the linear regression model based on the wavelet transform. This method is motivated by the observation that there exists a distinctive correlation between the sample images, belonging to the same kind of texture, at different frequency regions obtained by 2-D wavelet packet transform. Experimentally, it was observed that this correlation varies from texture to texture. The linear regression model is employed to analyze this correlation and extract texture features that characterize the samples. Therefore, our method considers not only the frequency regions but also the correlation between these regions. In contrast, the pyramid-structured wavelet transform (PSWT) and the tree-structured wavelet transform (TSWT) do not consider the correlation between different frequency regions. Experiments show that our method significantly improves the texture classification rate in comparison with the multiresolution methods, including PSWT, TSWT, the Gabor transform, and some recently proposed methods derived from these. - Reference